Integration Architecture · Interactive Guide

Data Flow Communication Patterns, Compared Live

Use the tabs, diagrams, and comparison matrix to stress-test REST APIs, message queues, and shared libraries before you pick the architecture that will protect DDA transaction history during batch failovers.

Published September 23, 2025 12 minute read Updated for Cross-Product API vs. DDA History

System Integration Patterns at a Glance

Modern digital ecosystems rarely survive on a single data flow pattern. The healthiest programs blend synchronous APIs, asynchronous events, and embedded libraries depending on latency targets, operational tolerance, and the coupling the business can support. This interactive guide elevates the conversation above personal preference so you can pair each integration with the scenario it was built for.

Data Flow Communication Methods

Service A

Producer

Communication

API · MQ · Library

Service B

Consumer

Rotate through the tabs to understand how each pattern changes your error budgets, staffing model, and ability to scale the DDA history platform without sacrificing resiliency.

Communication Patterns Deep Dive

RESTful API Calls

Synchronous HTTP-based communication where services expose endpoints that downstream teams can call directly. Data is requested and returned in real-time, which keeps teams closely aligned but adds live dependency risk.

Pros

Simple to understand and implement
Real-time data exchange
Direct request-response pattern
Easy debugging and monitoring
Technology agnostic

Cons

Tight coupling between services
Network latency on every call
Single points of failure
Harder to scale under high load
Synchronous blocking calls

Message Queue (Kafka · RabbitMQ)

Asynchronous messaging where producers publish events and consumers process them when ready. Queues decouple sender and receiver, letting each scale independently while embracing eventual consistency.

Pros

Loose coupling between services
High throughput capability
Built-in retry and dead-letter queues
Horizontal scalability
Event-driven architecture support

Cons

Additional infrastructure complexity
Eventual consistency trade-offs
Difficult to trace distributed flows
Message ordering constraints
Operational overhead

Shared Library Integration

In-process code sharing where reusable components are packaged as libraries and compiled into each service. Latency is minimal and deployments are simple, but versioning discipline becomes the make-or-break factor.

Pros

Lowest latency (in-process)
No network overhead
Code reuse and consistency
Simpler runtime operations
Transactional integrity

Cons

Tight coupling at build time
Coordinated deployments required
Language and framework lock-in
Versioning complexity
Harder to scale independently

Service Orchestration Patterns

Orchestration vs. Choreography

Orchestration (Central Control)

Orchestrator

Business Brain

Service A

Service B

Service C

Choreography (Event-Driven)

Service A

Event Bus

Service B

Orchestration Pattern

A central orchestrator coordinates downstream calls and business rules. You gain visibility and deterministic flows, but the orchestrator becomes a reliability and scalability choke point if it is not engineered with redundancy.

Pros

Centralized business logic
Easier to trace and debug
Coordinated error handling
Simplified testing

Cons

Single point of failure
Potential performance bottleneck
Tight coupling to orchestrator

Choreography Pattern

Services exchange events without a central coordinator. Each bounded context publishes and reacts to domain events, unlocking scale and autonomy while increasing the need for strong observability and eventual consistency playbooks.

Pros

Loose coupling
High scalability
No single point of failure
Independent service evolution

Cons

Complex to trace workflows
Challenging error handling
Eventual consistency concerns

Detailed Comparison Matrix

This matrix sharpens the decision lens. Start with latency and failure handling—two metrics that directly influence customer experience during SOR outages—and then zoom into operational overhead to understand what your run teams will inherit.

Criteria	RESTful API	Message Queue	Shared Library
Latency	Medium (100–500 ms)	High (seconds to minutes)	Lowest (microseconds)
Throughput	Medium	Very high	Very high
Coupling	Runtime coupling	Loose coupling	Build-time coupling
Scalability	Moderate	Excellent	Limited
Reliability	Medium	High (with retry/DLQ)	High (in-process)
Complexity	Low	High	Medium
Technology Independence	High	High	Low
Operational Overhead	Medium	High	Low
Data Consistency	Strong (synchronous)	Eventual	Strong (transactional)
Failure Handling	Immediate failure propagation	Built-in retry & DLQ	Application-level recovery

Your Cross-Product API Use Case Analysis

Context: DDA Transaction History Integration

Three competing options surfaced during the SOR failover discovery work. Use the highlights below to align timeline pressure with infrastructure reality, then explore the recommended Option B orchestration route.

🅰️ Option A: Direct SOR Integration in Cross-Product API

Pattern: RESTful API + database integration
Timeline: 16 sprints (March 2026)
Assessment: Long-term target state with significant complexity, engineering lift, and timeline risk.

🅱️ Option B: Consumer Orchestration (Bifurcated Architecture)

Pattern: Service orchestration with dual API calls
Timeline: Meets 11/30 deadline
Assessment: Pragmatic interim solution with transparent technical debt and fast runway to production.

🅲 Option C: Library Integration (Rejected)

Pattern: Shared library with routing logic
Timeline: 8 sprints (January 2026)
Assessment: Elegant on paper but blocked by MQ infrastructure constraints and connection limits.

Option B: Recommended Architecture Flow

Digital Channels

Orchestrator

DDA History API

List Views · SOR Resilient

AND

Search Experience

SSE Team

Cross-Product API

Search & CDA · Multi-account

Key Considerations for Your Decision

Timeline Pressure

The Q1 2026 milestone is non-negotiable. Option B extends the proven integrations you already run in production, giving delivery leads a credible path to the November 30th checkpoint.

Infrastructure Constraints

Mainframe MQ connection limits blocked Option C. Roughly 40 Cross-Product instances would demand persistent MQ channels that infrastructure cannot provision without risking stability.

Strategic Evolution

Long-term ambition remains a unified Cross-Product “Uber API.” Option B protects current delivery while the utilities-only architecture and AWS optimization efforts move forward.